Design Decisions That Determine Townhouse Density

 

 

A townhouse is a dwelling unit attached to but not stacked above others in a common building shell. It is often called a rowhouse for this reason. The physical shell, however, may be converted and occupied by any other activity when in conformance with local building and zoning codes.

The capacity of land to accommodate gross building area for any activity begins with the primary parking system adopted. Based on this definition, there are only six building classification categories on the planet.

1)      G1: Buildings with adjacent surface parking on the same premise

2)      G2: Elevated buildings over surface parking

3)      S1: Buildings with an adjacent parking garage on the same premise

4)      S2: Buildings with an underground parking garage

5)      S3: Buildings over a parking garage

6)      NP: Buildings with no parking required

These 6 categories constitute the Shelter Division in the Urban and Rural Phyla of a Built Domain that is one of two worlds on a single planet.

All residential land use activity falls into one of three classification categories and occupies one of the 6 building design categories mentioned above.

1)      R1: Single-family detached dwelling units

2)      R2: Single-family attached and spread dwelling units that are not elevated above a garage. (townhouses, twin-singles, four-family, and so on)

3)      R3: Single-family attached and stacked dwelling units (apartments)

The combination of the R2 occupant arrangement within the G1 building category shell is referred to as the G1.R2 Activity Group.

The purpose of this discussion is to illustrate that site planning decisions involve too many correlated variables to be led by a few uncorrelated zoning regulations.

The design decisions that determine G1.R2 townhouse density are identified by the 68 gray cells in the Table 1 forecast model. Only 4 of these topics and 15 gray cell items are generally addressed by a zoning ordinance. The remaining 53 are discretionary. This encourages arbitrary leadership decisions that have never been measured or evaluated for the physical, social, psychological, environmental, and economic quality of life that results. We only know that we are creating sprawl in an attempt to escape the excessive intensity, deteriorating conditions, and market acceptance of decline within our cities; but cannot determine with our current measurement tools if revised percentages of shelter capacity, activity occupancy, and intensity will produce lifestyle improvement over time as maintenance expense increases.

Table 1 applies to the G1.R2 Activity Group and illustrates that the density calculated in cell J55 is a function of the 68 mathematically correlated gray cell values entered above. The only values typically led by zoning regulation can be found in the 15 gray cells of columns D, H, and L of the Townhouse Module. The remaining 53 are discretionary but needed to find the density value in cell J55. Unfortunately, density does not clearly explain the shelter capacity, intensity, intrusion, and physical dominance implied by these decisions. These measurements are unknown; and they cannot be related to a reference library of accumulated knowledge until consistent measurement and evaluation of existing conditions is compiled.

In fact, under current zoning regulations, it is possible to decrease density to meet a requirement while increasing the physical intensity created. For instance, if I increased the habitable areas planned in column C of the Townhouse Module and held all other values constant, the total number of dwelling units predicted in cell K53 for the land area given would decline from 147 to 137. This would reduce the density calculated in cell J55 from 8.98 to 8.34 but increase the total building area planned in cell L53 from 205,284 to 219,039 sq. ft. This increases the physical intensity calculation from 0.115 to 0.123 in cell D61. In other words, the number of families would decline but the physical proximity of the buildings would increase on the same land area. This indicates the social nature of density measurement and its inability to lead the physical characteristics of shelter intensity within cities. Confusion over this distinction has left innumerable loopholes in zoning ordinances illustrated by the 53 discretionary gray cells values mentioned. The values in these cells can be adjusted to increase profitability while ignoring the unknown consequences of excessive intensity. This lack of leadership simply encourages our intuitive, sprawling search for relief.

The percentage of unpaved open space requested in cell F11 of Table 1 and the dwelling unit mix specified in columns B and C of Its Townhouse Module are two design decisions topics that often remain unspecified in a zoning ordinance. I’ve prepared Table 2 to explain the random implications associated with this degree of flexibility. The examples are only a few of the many that could be made by exploiting the loopholes a zoning ordinance creates with the omission of pivotal design topics, decisions, and correlation.

The omission of dwelling unit area specifications in a zoning ordinance created the opportunity described above and recorded in column D of Table 2. Gray cell D2 in this table notes the change made in Table 1 to produce the results in cells D5-D8. Columns E-H in Table 2 record the results produced in Table 1 by the unregulated changes noted in the gray cells of Table 2. Density and intensity vary in unison in Table 2 because all 68 design decisions are mathematically correlated in Table 1. In reality, density could vary to a much greater degree in Row 5 because current zoning regulations are not complete or mathematically correlated. This arbitrary approach was the best we could do for decades and was justified by our inability to comprehensively classify, itemize and mathematically correlate the design decisions involved with schematic site planning.

I hope I’ve made it clear that anything can happen when one or more values related to any of the gray cell design decisions in Table 1 are modified without prior understanding. This has compromised our ability to lead change because we have not been able to classify results within a spectrum of intensity that can be used to shelter growing populations within geographic limits that protect their source and quality of life.

The challenge is to correlate a city’s land areas with the building capacity, intensity, and activity needed to produce an average economic yield per acre equal to the total average expense per acre it needs to provide the quality of life it desires. A mismatch simply produces decline and sprawling attempts to adjust revenue without an understanding of the economic yield per acre produced by combinations of shelter area, capacity, intensity, and activity within cities. It cannot be done without mathematics and relational databases.

I’ve previously written an essay entitled, “The Decisions That Determine Apartment Density”. This essay has addressed townhouses. They’re both part of the Residential Activity Group, but these references can be confusing without an overview of the building classification system. I’m including the Table of Contents of my new book, The Equations of Urban Design, to provide this overview. (Keep in mind that a land use activity may occupy any building design category when both comply with local building and zoning codes. The fact that there are many activities but few building design categories makes shelter capacity and intensity measurement, evaluation, and prediction useful, since shelter capacity must be present before occupant activity can contribute revenue per sq. ft. of shelter and economic yield per acre of incorporated area.)